Suitability of Early Blight Forecasting Systems for Detecting First Symptoms in Potato Crops of NW Spain

• Published in: Agronomy (Basel) Vol. 12; no. 7; p. 1611
• Main Authors: Meno, Laura, Abuley, Isaac Kwesi, Escuredo, Olga, Seijo, M. Carmen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-07-2022
• Subjects: Alternaria spp; Artificial intelligence; Blight; Calendars; Crop diseases; Crops; Decision support systems; Disease control; disease forecasting; Early blight; Epidemics; Farmers; Flowering; Forecasting; Fungal diseases; Fungicides; Growing Degree Days; Infections; Pathogens; Pesticides; physiological age; Plants (botany); Potatoes; Rain; Senescence; Signs and symptoms; Solanum tuberosum; Weather; Weather forecasting; Spain
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy12071611

In recent years, early blight epidemics have been frequently causing important yield loses in potato crop. This fungal disease develops quickly when weather conditions are favorable, forcing the use of fungicides by farmers. A Limia is one of the largest areas for potato production in Spain. Usually, early blight epidemics are controlled using pre-established schedule calendars. This strategy is expensive and can affect the environment of agricultural areas. Decision support systems are not currently in place to be used by farmers for managing early blight. Thus, the objective of this research was to evaluate different early blight forecasting models based on plant or/and pathogen requirements and weather conditions to check their suitability for predicting the first symptoms of early blight, which is necessary to determine the timings of the first fungicide application. For this, weather, phenology and symptomatology of disease were monitored throughout five crop seasons. The first early blight symptoms appeared starting the flowering stage, between 37 and 40 days after emergence of plants. The forecasting models that were based on plants offered the best results. Specifically, the Wang-Engel model, with 1.4 risk units and Growing Degree-Days (361 cumulative units) offeredthe best prediction. The pathogen-based models showed a conservative forecast, whereas the models that integrated both plant and pathogen features forecasted the first early blight attack markedly later.